1. Field of the Invention
The invention relates to the use of infrared thermographic imaging in live animals and carcasses to predict lean and fat content and composition.
2. Description of the Related Art
The estimation of tissue composition and proportion in live animals, particularly lean body mass (skeletal muscle), is difficult, and some form of allometric measurement using such parameters as weight function or surface area or skin thickness (Neilson and Jensen, 1972; Kleiber, 1975) must often be used. Ultimately, the total dissection of muscle mass, in laboratory and domestic animals, is still the most accurate method of determining lean body mass.
The ability to determine lean body mass and other tissue composition characteristics in a non-invasive manner would have substantial utility including the measurement of the physiological response of an animal to a nutritional or endocrine treatment, the prediction of an athlete's body composition in response to a training regime, sire-dam selection criteria in animal breeding programs, and carcass or lean yield predictions for slaughter animals upon which reward rates (payments) are substantially based. Given the growing concern over the consumption of saturated fats, the ability to evaluate and classify live animals and/or carcasses for lean:fat ratios would have considerable application in a value based marketing system in the animal industries (Forrest, 1995) where current visual assessment systems are considered outdated.
The measurement of lean body mass in animal carcasses has been attempted, and as discussed by O'Grady (1989) and Forrest (1995) these techniques include the use of magnetic resonance imaging spectrometry, x-ray computed tomography, visual image analysis, several types of ultrasound, electromagnetic scanning, neutron activation analysis as well as the use of optical, ultrasonic and mechanical probes. These techniques all display utility to varying degrees but all have disadvantages in terms of either cost, technical difficulties, accuracy, reliability or speed of operation. Moreover, these aforementioned techniques are predominantly conducted on the animal carcass and are thus of limited predictive value for live animals.
For lean mass assessment in live animals there are very few techniques available. Some use of electronic and tomography methods have been developed as well as ultrasound and tracer dilution techniques. Again, these procedures have usually proven to be too costly, technically difficult to operate, lacking in accuracy, or too invasive and slow to use on a large scale.
Infrared thermography is an alternative technique that has not been explored. Infrared thermography has been used in human medicine for some time for the diagnosis and study of conditions such as tumours and cardiovascular integrity (Clark and Cena, 1972) as well as hyperthermia (Hayward et al., 1975). In domestic animals, infrared thermography has also been found useful for diagnosing vascular lesions in pigs (Lamarque et al., 1975) and leg injuries in horses (Clark et al., 1972). The patent literature also discloses the use of infrared thermography for several purposes including the determination of fat content in meat post mortem (U.S. Pat. No. 3,877,818 to Button et al.), the detection of a cow in heat (U.S. Pat. No. 3,948,249 to Ambrosini), and to measure temperature differentials (U.S. Pat. No. 5,017,019 to Pompei). U.S. Pat. No. 5,474,085 to Hurnik et al. describes a method for remotely obtaining thermographic images of groups of live animals in an area such as a pen, distinguishing each animal from every other animal and the background, and using information derived from the thermographic image to determine the weight of each animal.
U.S. Pat. No. 5,458,418 to Jones et al. describes a method for detecting a high probability of producing poor meat quality in live domestic livestock, comprising the steps of:
(a) scanning the live animal with an infrared camera to produce a thermographic image; PA1 (b) for cattle, determining the proportion of the scan falling within the test temperature range of 28-32.+-.2.degree. C.; PA1 (c) for swine, determining the proportion of the scan falling within the test temperature range of 24-26.+-.2.degree. C.; and PA1 (d) rejecting the animal as one having a high probability of producing poor meat quality if the proportion of the scan falling within the test temperature range is lower than that falling outside the test temperature range. PA1 (a) obtaining either or both of at least one infrared thermographic image of the animal while it is alive, taken from at least one view, and at least one infrared thermographic image of the carcass of the animal after slaughter, taken from at least one view, each thermographic image being capable of being represented as an array of pixels providing temperature data representative of temperature information at the corresponding part of the image; PA1 (b) calculating the value of at least one statistical measure of the temperature data for each thermographic image; PA1 (c) providing a predictive model wherein the tissue composition characteristic is included as an output variable, and the statistical measures of temperature data for each thermographic image are included as input variables; and PA1 (d) solving the predictive model to provide the value of the tissue composition characteristic. PA1 image acquisition means for obtaining either or both of at least one infrared thermographic image of said animal while it is alive, taken from at least one view, and at least one infrared thermographic image of the carcass of the animal after slaughter, taken from at least one view; PA1 computing and storage means for: PA1 output means for providing an output of the value of the tissue composition characteristic.
U.S. Pat. No. 5,595,444 to Tong et al. describes a method for detecting poor meat quality in groups of live animals. Animals from a group of live domestic animals are scanned to produce thermographic images. The images are statistically analyzed to determine a measure of central tendency such as the mean temperature for each animal's image and for the group. A measure of dispersion from the measure of central tendency, such as standard deviation, is determined for the group. Then, animals are rejected as having a high probability of producing poor meat quality if the measure of central tendency for that animal's temperature differs from the measure of central tendency for the group by more than 0.9 standard deviations. Alternatively, up to 20% of animals are rejected, being those animals whose measures of central tendency differ the most from the measure of central tendency for the group.
However, previous research was insufficient to teach any application of infrared thermography for determining tissue composition characteristics such as lean body mass. In fact, conventional wisdom on the matter of creating predictive indexes for lean body mass discourages using any kind of temperature measurement. It is generally thought that measurement of carcass temperature is not practical in an industrial setting and that carcass temperature is uniform enough to eliminate it as one of the independent variables in a predictive model (Forrest, 1995).
Therefore, there remains a need for an accurate, inexpensive, non-invasive process for predicting tissue composition characteristics such as lean body mass in domestic animals.